Abstract

This article provides a neurobiological account of symptoms that have been called 'hysterical', 'psychogenic' or 'medically unexplained', which we will call functional motor and sensory symptoms. We use a neurobiologically informed model of hierarchical Bayesian inference in the brain to explain functional motor and sensory symptoms in terms of perception and action arising from inference based on prior beliefs and sensory information. This explanation exploits the key balance between prior beliefs and sensory evidence that is mediated by (body focused) attention, symptom expectations, physical and emotional experiences and beliefs about illness. Crucially, this furnishes an explanation at three different levels: (i) underlying neuromodulatory (synaptic) mechanisms; (ii) cognitive and experiential processes (attention and attribution of agency); and (iii) formal computations that underlie perceptual inference (representation of uncertainty or precision). Our explanation involves primary and secondary failures of inference; the primary failure is the (autonomous) emergence of a percept or belief that is held with undue certainty (precision) following top-down attentional modulation of synaptic gain. This belief can constitute a sensory percept (or its absence) or induce movement (or its absence). The secondary failure of inference is when the ensuing percept (and any somatosensory consequences) is falsely inferred to be a symptom to explain why its content was not predicted by the source of attentional modulation. This account accommodates several fundamental observations about functional motor and sensory symptoms, including: (i) their induction and maintenance by attention; (ii) their modification by expectation, prior experience and cultural beliefs and (iii) their involuntary and symptomatic nature.

This figure provides a schematic overview of the message passing scheme usually presented as a neurobiologically plausible implementation of predictive coding. In these schemes, neurons are divided into prediction (black) and prediction error (red) units that pass messages to each other, within and between hierarchical levels in the cortex. Superficial pyramidal cells (red) send forward prediction errors to deep pyramidal cells (black), which reciprocate with predictions that are conveyed by (polysynaptic) backward extrinsic connections. These are functions of conditional expectations encoded by the activity of the prediction units. This process continues until the amplitude of prediction error has been minimized and the predictions are optimized in a Bayesian sense. The prediction errors are the (precision weighted) difference between conditional expectations encoded at any level and top down or lateral predictions. Note that there are prediction errors at every level of the hierarchy. Crucially, the potency of prediction errors at any level of the hierarchy depends upon their precision (blue arrows), which effectively modulates or weights the prediction error. The synaptic infrastructure proposed to mediate this comparison and subsequent modulation is shown in the insert, in terms of a doubly innervated synapse that is gated by dopamine (blue). Here, dopamine is delivered by en passant synaptic boutons and postsynaptic D1 receptors have been located on a dendritic spine expressing asymmetric (excitatory) and symmetric (inhibitory) synaptic connections.

A heuristic illustration of Bayesian inference in terms of a likelihood distribution, a prior distribution and the resulting posterior distribution. All these distributions are functions of some hidden state or cause of observed data, where the likelihood and prior distributions constitute a generative model. The important issue to observe here is that as the precision (certainty) of the prior increases, it draws the posterior estimate towards it; and away from the likelihood distribution. Here, precision corresponds to the inverse variance or dispersion (width) of the distributions, indicated with the blue arrows. Under models with additive Gaussian noise, the precision of the likelihood corresponds to the inverse amplitude of the noise (the signal-to-noise ratio).

Here we illustrate schematically our proposal with regard to generation of functional sensory symptoms. In healthy subjects, we suggest that an intermediate hierarchical level involved in sensation (e.g. secondary sensory cortex) integrates bottom-up prediction error related to incoming sensory data (red arrows) and top-down priors about the causes of sensory data (black arrows) in an optimal way. Top: The interaction between the likelihood of sensory data and the prior beliefs over those data at this intermediate hierarchical level, which results in a posterior distribution corresponding to the percept or posterior belief. By this simplification, we do not mean that the percept occurs only at the intermediate level; in reality, its physical representations are likely to be distributed across several levels. The x-axes of the graphs in the top panels denote ‘the amount of sensation’; from none to a maximum. In FMSS, we propose that an abnormal prior belief related to sensation in the relevant domain (here illustrated as the insular cortex for pain and the secondary sensory cortex for non-painful sensation) is afforded too much precision via misdirected attentional gain from higher hierarchical levels (thick blue arrow). This increase in precision (synaptic gain) causes a shift in the posterior distribution towards the prior expectation, overwhelming the influence of bottom-up prediction errors (dotted red arrow). This results in a percept that matches the prior beliefs encoded by the intermediate level, which is impervious to bottom-up prediction errors. At the same time, a precise prediction error is returned to higher levels to excite higher-level representations or explanations for the abnormal percept—again these pathologically boosted prediction errors dominate over prediction errors at the higher level when competing to influence high-level beliefs. These beliefs may include perceptual attributes—like agency. Note that we are not proposing that increased attention to secondary sensory cortex (SII; for example) per se causes anaesthesia; the crucial factor is the pre-existence of an abnormal prior belief predicting anaesthesia, whose precision is then increased by attention to that area. The existence of a different prior belief in secondary sensory cortex would lead to a different percept. Forward connections conveying prediction error are in red, backward connections conveying predictions are in black and the descending attentional modulatory connections are in blue. Superficial pyramidal cell populations encoding prediction error are shown as red triangles while deep pyramidal cells encoding posterior expectations are depicted as black triangles. Acg = anterior cingulate; Ins = insula; Prc = precuneus; SI = primary sensory cortex; SII = secondary sensory cortex.

Here we provide a schematic that summarizes our theory. The 3D coloured plot represents an intermediate level within the nervous system, e.g. secondary sensory cortex, supplementary motor area or insular cortex. The plot represents the probability over different hidden states, arranged on a grid, where certain states are predicted to be more probable (peaks/red tones) and some are less probable (troughs/blue tones). We propose that the primary problem is the formation of an abnormal prior at this level originally instantiated via a number of interacting factors (not all of which are mentioned here and which will be different for different patients). The formation of this abnormal prior belief (peak) is mediated by the misdirection of attention (illustrated by a self-directed attentional ‘spotlight’), which affords it abnormal precision. This changes the strength of the abnormal prior, so that it drives perception or action consistent with it through top-down effects and calls for a higher-level explanation (e.g. agency) through bottom-up effects. Bottom: When attention is diverted, the precision of the abnormal prior is reduced so that it no longer drives perception or action. A crucial point is that the hierarchically higher sources of attention do not predict the precise dynamics of the percepts they induce. The resulting prediction error is explained (rationally and within the cognitive/affective framework of the individual) as an involuntary symptom of illness.